A size-resolved chemical mass balance (SR-CMB) approach for source apportionment of ambient particulate matter by single element analysis

Dai, Qili; Bi, Xinhui; Huangfu, Yanqi; Yang, Jialin; Li, Tingkun; Khan, Jahan Zeb; Song, Congbo; Xu, Jiao; Wu, Jianhui; Zhang, Yufen; Feng, Yinchang

doi:10.1016/j.atmosenv.2018.10.026

Cited by 17 publications

(9 citation statements)

References 44 publications

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“…Figure 6. RCC profiles of PM 2.5 collected using the DTSM with (a) data collected from available published profiles (Ge et al, 2004;Kong, 2014;Liu et al, 2016Liu et al, , 2017Yan et al, 2017a;Dai et al, 2019), and the coal fly ash RSM with (b) data collected from Wang et al, 2016. eycomb briquette coals. Generally, OC and sulfur are the predominate species in PM 2.5 emitted from RCC.…”

Section: Residential Coal Combustion (Rcc)mentioning

confidence: 99%

“…RCC profiles of PM 2.5 emissions from chunk coal and honeycomb briquette coals. Data were collated from published data (Ge et al, 2004;Kong, 2014;Liu et al, 2016Liu et al, , 2017Yan et al, 2017b;Dai et al, 2019). industrial emissions (cement plants, coking plants, and steel plants) (Ma et al, 2015;Qi et al, 2015;Yan et al, 2016;Zhao et al, 2015a).…”

Section: Industrial Process Emissionsmentioning

confidence: 99%

“…With the development of advanced sampling and chemical analysis techniques, more valuable information has been explored to further expand the existing profiles or create new profiles, including data on the following: organic compounds (Schauer and Cass, 2000;Simoneit et al, 1999); isotopic measurement of radiocarbon (Wang et al, 2017), sulfur (Han et al, 2016), and nitrogen (Pan et al, 2016); high-resolution aerosol mass spectra (Zhang et al, 2011); and particle size distribution (Zhou et al, 2004). This information has been proven to provide source specificity capable of being incorporated into receptor models as new markers (Zheng et al, 2002), constraining source contributions (Amato et al, 2009), and developing new models (Ulbrich et al, 2012;Dai et al, 2019). For example, developed a size-resolved CMB approach for the source apportionment of PM based on the size profiles of sources.…”

Section: Introductionmentioning

confidence: 99%

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Feng¹

2018

Preprint

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Section: Residential Coal Combustion (Rcc)mentioning

confidence: 99%

Section: Industrial Process Emissionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feng¹

2018

Preprint

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“…33−35 CMB is the model of choice when the types and chemical compositions of the potential sources have been predetermined. 26,36,37 In the absence of a priori knowledge of source component compositions, APCS-MLR, PMF, and Unmix can identify and quantify the sources' contributions to the samples according to the correlations among the concentrations of various pollutants observed. 38−41 Among the classical multivariate receptor models, PMF and Unmix, which were developed by the U.S. Environmental Protection Agency for use in air quality management originally, 42,43 have been very popular in source apportionment applications as they are easy to access and use.…”

Section: ■ Introductionmentioning

confidence: 99%

Systematic Evaluation of Two Classical Receptor Models in Source Apportionment of Soil Heavy Metal(loid) Pollution Using Synthetic and Real-World Datasets

Yang

Cheng

et al. 2022

Environ. Sci. Technol.

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Due to the lack of a priori knowledge on true source makeup and contributions, whether the source apportionment results of Unmix and positive matrix factorization (PMF) are accurate cannot be easily assessed, despite the availability of built-in indicators for their goodness of fit and robustness. This study systematically evaluated, for the first time, the applicability and reliability of these models in source apportionment of soil heavy metal(loid)s with synthetic datasets generated using known source profiles and contributions and a real-world dataset as well. For eight synthetic datasets with different pollution source characteristics, feasible Unmix solutions were close to the true source component compositions (R 2 > 0.936; total mean squared errors (MSEs) < 0.04), while those of PMF had significant deviations (R 2 of 0.484–0.998; total MSEs of 0.04–0.16). Nonetheless, both models failed to accurately apportion the sources with collinearity or non-normal distribution. Unmix generally outperformed PMF, and its solutions showed much less dependence on sample size than those of PMF. While the built-in indicators provided little hint on the reliability of both models for the real-world dataset, their sample-size dependence indicated that Unmix probably yielded more accurate solutions. These insights could help avoid the potential misuse of Unmix and PMF in source apportionment of soil heavy metal(loid) pollution.

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“…The ambient PM pollution leads to air quality degradation, haze formation, reduced visibility, climate change, global warming, ecosystem deterioration, and human health risks (Ari et al, 2020;Cesari et al, 2020;Jain et al, 2020). The ambient PM originates from diverse primary sources (anthropogenic and natural), as well as in the form of secondary particles formed by the photochemical processes involving both the anthropogenic and natural precursors (Dai et al, 2019;Zalakeviciute et al, 2020). The ambient PM is a complex mixture ranging across various diameter sizes having different source origins, formation processes, physiochemical characteristics, and health effects.…”

Section: Introductionmentioning

confidence: 99%

Size Distribution of Ambient Particulate Matter and Its Constituent Chemical Species Involving Saccharides During Early Summer in a Chinese Megacity

Khan

Sun

Tian

et al. 2021

Front. Environ. Sci.

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The ambient particulate matter (PM) pollution adversely influences the human health and natural environment. The size distribution of ambient PM determines the physiochemical and optical properties of ambient aerosol, whereas it reflects the variability in local and regional PM emission sources and formation mechanisms. In the present work, the size distribution and characteristics of the carbonaceous, ionic, elemental, and saccharide species were categorically investigated for the fraction-wise PM in Tianjin during 2018 early summer. The average concentrations were 32.4, 20.9, and 49.3 μg/m3 for the PM1, PM1–2.5, and PM2.5–10, respectively. The coarse PM2.5–10 accounted for most of the PM10 mass (47%), followed by the fine PM1 (33%) and intermodal PM1–2.5 (20%). The carbonaceous and ionic species exhibited bimodal distribution and were distributed mostly to the fine size fraction and then to the coarse size fraction. The elemental species exhibited unimodal distribution and were distributed mostly to the coarse size fraction. The specific saccharide species indicated the significant contribution of biomass burning and primary biogenic emissions. The bimodal mass size distribution of levoglucosan indicated the significant biomass burning contributions to the fine and coarse size fractions. The unimodal glucose, fructose, and arabitol distribution and the bimodal mannitol distribution indicated the dominant primary biogenic contributions to the coarse size fraction. The PM1/PM10, PM1–2.5/PM10, PM2.5–10/PM10, OC/EC, SOC/OC, AE/CE, NO3–/SO42–, K+/EC, and levoglucosan/K+ ratios were used to investigate the characteristics of the ambient size-fractionated PM. The anthropogenic sources (combustion processes, traffic emissions, and secondary particles, etc.) contributed mostly to the fine PM1 and intermodal PM1–2.5 fractions, whereas the natural sources (primary biogenic, marine salt, and mineral dust, etc.) contributed mostly to the coarse PM2.5–10 fraction. This work is a significant addition to the multi-size ambient PM’s size distribution and characterization studies.

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A size-resolved chemical mass balance (SR-CMB) approach for source apportionment of ambient particulate matter by single element analysis

Cited by 17 publications

References 44 publications

Response to RC3

Response to RC3

Systematic Evaluation of Two Classical Receptor Models in Source Apportionment of Soil Heavy Metal(loid) Pollution Using Synthetic and Real-World Datasets

Size Distribution of Ambient Particulate Matter and Its Constituent Chemical Species Involving Saccharides During Early Summer in a Chinese Megacity

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